Strategic marketing focuses on the concepts and processes involved in developing market-driven strategies. This course examines the marketing management concepts underlying both consumer and industrial marketing strategy and tactics. It covers major marketing decisions in a problem oriented setting, the in-depth study of general marketing management and the development of marketing plans and strategies. It illustrates how marketing management varies the marketing mix (price, product, promotion, and place) to achieve maximum consumer satisfaction. Emphasis is placed on marketing strategy (formulation and implementation) and the role of the firm vis-a-vis its various environments (socio-political-economic).
In the digital age, information abundance creates new opportunities for firms to understand and assess the outcome of their marketing strategies. The broad objective of this course is to provide a fundamental understanding of marketing research methods employed by well-managed firms. The course focuses on integrating problem formulation, research design, questionnaire construction, sampling, data collection, data analysis, AI methods in marketing to yield the most valuable information. The course also examines the proper use of statistical applications, with an emphasis on the AI application of consumer data and the interpretation and use of results.
Consumer Behaviour is the study of the processes involved when individuals or groups select, purchase, use, or dispose of products, services, ideas, or experiences to satisfy their needs and desires. By the completion of this subject, students will be able to explain the major stages which consumers usually go through when making a consumption-related decision; identify the major individual, social and cultural factors that affect consumers’ decision making; and assess marketing implications for practitioners.
The purpose of this course is to provide students with a deep understanding of current big data approaches and marketing applications. The topics include trends of big data applications, consumer evolution in the digital age, big data insights into business, text mining and topic modeling, Web search data and Internet marketing, social network and social media marketing, mobile marketing, and data driven marketing strategy. Methodologies and techniques, including text analysis, Web crawling, logistic regression, and social network analysis, will be introduced and their business applications will be explained. This course aims to help students develop analytics skills and abilities combined with innovative business ideas to create effective big-data marketing strategies in today’s marketing.
Theoretical and practical appreciation of the role of “integrated marketing communication” (IMC) in today’s business environment. IMC differs from traditional advertising and promotion programmes by using zero-based planning, data-driven communication and brand touch points. The course focuses on using strategic mix of advertising, sales promotion, public relations, event marketing and direct response promotions along with mass and two-way communication in the digital age.
The course aims at providing participants with a basic understanding on what customer relationship management (CRM) is, why it is so important in the contemporary business world, and how it is implemented using recent information technology. In addition to the traditional lecturing method, this course will include a lot of case analyses, small group discussions, and/or presentations. Specifically, the following topics will be covered: - customer heterogeneity, - customer lifetime value, - customer dynamism, - CRM-based marketing strategies, - and one-to-one marketing. The implementation of CRM via Internet marketing, data warehouse, data mining, and database marketing will also be discussed.
The global top 100 companies are using at least one digital and social media platforms to promote their brands and build stronger connections with their target customers. Definitely, digital and social media marketing has emerged as a tectonic shift from traditional marketing. This course introduces concepts, theories and applications of digital and social media marketing strategies in the Greater China and around the globe. With an in-depth study of display advertising, search advertising and social media marketing, students would be able to implement digital and social media strategies which involve different aspects of marketing. Due to a strong need for marketing professionals who are attuned to this area, this course is specifically for students who are planning to enter digital and social media marketing, consulting and brand management roles.
This course presents students with a foundational understanding of state-of-the-art artificial intelligence (AI) technologies and their marketing implications as well as their limitations. We will cover three key AI technologies: machine learning, natural language processing, and robotics and discuss their marketing applications. Students will gain a practical introduction to these key AI technologies and their marketing implications. The course does not assume any particular technological background, though some programing knowledge is a plus. Students will focus on the marketing and managerial implications of these technologies and how they can be applied in the workplace. In addition, students will have the opportunities to learn how to apply these AI technologies using real marketing dataset.
The course will provide an overview of AI from theory to practical. Student will learn what is AI and how it can be integrated into businesses from identify marketing potential to chatbot customer communication. The course will teach students how to use AI natural language classification services to build chatbots/virtual agents across of marketing channels and touchpoints to support sales and marketing; using image recognition to tag and classify visual content to support visual listening and unlock hidden value in unstructured data using the natural language understanding to find answers, monitor trends, and surface patterns.
Internet of Things (also referred to as IoT) is an Internet technology that extends Internet connectivity beyond standard devices, such as desktops, laptops, and smartphones, to any range of everyday objects including traditionally non-internet-enabled physical devices. By combining low-power, battery-free hardware with real-time digital analytics, IoT disrupts the traditional retail process and enables retailers to transform their customer service relationships and provide consumers with a seamless shopping experience, while meeting, and surpassing, the expectations of increasingly tech-savvy consumers. This course introduces the key concepts of IoT and its applications in Retail industry.
Design thinking is an essential tool for simplifying and humanizing. The principles of design thinking include a focus on users’ experiences. It is a multi-step method for understanding users’ needs better, creating innovative solutions for them and iterating quickly to get it just right. In the course, students will learn how to use the physical models such as diagrams and sketches, to explore problems and the use of prototypes to experiment with solutions to create more relevant marketing.
The course provides the tools necessary to analyse the opportunities and potential competitive threats in commercial Web-based organizations. To quantify and apply the analysis, a particular focus is on valuing Internet companies based on a careful examination of their business model and environment. The course also covers the basic theory of financial intermediation as it applies to online financial service firms. It discusses the impact of a migration to online financial services and the competitive changes created.
This course is an introductory course on data mining. It introduces the basic concepts, principles, methods, implementation techniques, and applications of data mining on marketing problems, with a focus on pattern discovery and cluster analysis, which are wildly used in marketing problems. Students will learn why pattern discovery is important, what the major strategies are for efficient pattern mining, and how to apply pattern discovery in some interesting marketing applications. Students will also learn the concepts and methodologies for cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. The objective of this course is to let students become acquainted with the strengths and limitations of various data mining techniques, and gain a deep understanding on the applications of data mining on marketing problems.
As the web technology and mobile use rapidly evolves, the volume of user-generated data expand exponentially. The distillation of knowledge from such a large amount of unstructured, dynamically changed data is an extremely difficult task without the help of distributed techniques. This course introduces most state-of-the-art Big data analytical concepts, techniques and tools. By taking this course, students will gain hands-on technical experiences on solving Big Data problems using distributed algorithms and tools widely adopted in industry. The topics include basic concepts about Big Data, installation and configuration of Hadoop and Spark under a multi-node environment, distributed algorithms (recommender systems, clustering, classification, topic models, and network analysis), web crawling and web data extraction using major application programming interfaces.
The course focuses on the collection, analysis, and use of web data for business intelligence. Topics covered include web analytics concepts, web analytics techniques, visitor activity analysis methods, and web intelligence fundamentals. The course also evaluates practical, real-work analysis cases to demonstrate the strategic uses of web analytics for business intelligence and proper application of analytics techniques for online data optimization.
Participants in the modern financial system (i.e., individuals, institutions, markets and regulators) increasingly make their decisions based on empirical evidence derived from data. In response to the ever-growing size and complexity of financial data, new technologies and methods have been developed in recent times to facilitate evidence-based decision making in the big-data environment. The aim of Financial Analytics is to present students with the process of identifying adequate big-data sources as well as deriving useful information from the data for solving a research/business problem in modern financial markets. A range of mainstream big-data technologies and methods are covered in this course including but not limited to automated data ETL, quantitative metric design and implementation, general purpose programming framework and cloud computing infrastructure.
Much of the global economy is increasingly dominated by services. This course focuses on challenges of managing service brands and delivering quality service to customers across industry sectors. The attraction, retention, and building of strong customer relationships through quality service (and services) are all at the heart of the course content. This course considers service excellence as a corporate strategic vision and views effective service strategy from an integrative perspective that covers customers, employees, and firm operations. This course is designed to help students recognize the vital role that services play in the economy and its future as well as acquire the necessary knowledge and skills to implement quality service strategies for competitive advantage across industries.
Over the last decades, the concept of brands and branding is increasingly important for companies in almost every industry, from consumer goods markets to business to business (B2B) organizations. In contemporary markets where competition is multifaceted, brands and brand equity are the most valuable strategic assets in developing effective marketing strategies, competitive advantage and long-term profitability.
This course is designed to acquaint students with how strong brands are created and managed, as well as what should be done to sustain and leverage their brands over time. Students learn to build and manage strong brands to help organizations compete in the marketplace. Conceptual theories and case studies will be used to enhance understanding of the application of knowledge in real world situations. Topics will cover from the meanings of brand, building strong brands in the global and digital world to specific categories of branding strategies, such as luxury branding.
The course objective is to equip students with quantitative and analytical skills to solve problems associated with data driven marketing. Students will learn how to formulate marketing problems regarding product, price, place, and promotion as mathematical and statistical models and how to solve them with data mining and statistical techniques. Applications of these models will be discussed.
The objective of this course is to provide students hand-on experience to solve big data marketing problems in the real-world environment. The course consists of lectures on big data software, big data group projects, meetings, reports and presentations. The big data project can be a data mining project or a text mining project or a voice mining project. Student work on the real world data and collaborate closely with professors to deliver joint solutions to the big data marketing problems in the industry.
The objective of the course is to help student to understand what machine learning is and how it can be used in marketing analytics. The course will first introduce to students the major machine learning algorithms that are commonly used in marketing and sales. It will also discuss real examples of using machine learning in marketing scenarios, such as personalizing offers to customers or improving an online customer experience. Students will also learn about the theory, techniques and how to choose the machine learning algorithm that best fits a particular marketing problem in industry.